
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go 
from plotly.offline import init_notebook_mode, iplot
%matplotlib inline
init_notebook_mode(connected=True) 
import warnings
warnings.filterwarnings('ignore')


df_movies = pd.read_csv("Data/movies_metadata.csv", low_memory=False)
df_credits = pd.read_csv("Data/credits.csv")
df_keywords = pd.read_csv("Data/keywords.csv")


df_movies.head()


df_credits.head()


df_keywords.head()


print(f"""The table 'movies' has {df_movies.shape[0]} rows and {df_movies.shape[1]} columns \
and contains all the details about certain movie.""")
print(f"""The table 'credits' has {df_credits.shape[0]} rows and {df_credits.shape[1]} columns \
and contains the information about the cast of the movie (main charachters, actors etc.) and \
the crew of the movie (director).""")
print(f"""The table 'keywords' has {df_keywords.shape[0]} rows and {df_keywords.shape[1]} columns \
and contains the keywords associated with the movie.""")

The table 'movies' has 45466 rows and 24 columns and contains all the details about certain movie.
The table 'credits' has 45476 rows and 3 columns and contains the information about the cast of the movie (main charachters, actors etc.) and the crew of the movie (director).
The table 'keywords' has 46419 rows and 2 columns and contains the keywords associated with the movie.


df_movies.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45466 entries, 0 to 45465
Data columns (total 24 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   adult                  45466 non-null  object 
 1   belongs_to_collection  4494 non-null   object 
 2   budget                 45466 non-null  object 
 3   genres                 45466 non-null  object 
 4   homepage               7782 non-null   object 
 5   id                     45466 non-null  object 
 6   imdb_id                45449 non-null  object 
 7   original_language      45455 non-null  object 
 8   original_title         45466 non-null  object 
 9   overview               44512 non-null  object 
 10  popularity             45461 non-null  object 
 11  poster_path            45080 non-null  object 
 12  production_companies   45463 non-null  object 
 13  production_countries   45463 non-null  object 
 14  release_date           45379 non-null  object 
 15  revenue                45460 non-null  float64
 16  runtime                45203 non-null  float64
 17  spoken_languages       45460 non-null  object 
 18  status                 45379 non-null  object 
 19  tagline                20412 non-null  object 
 20  title                  45460 non-null  object 
 21  video                  45460 non-null  object 
 22  vote_average           45460 non-null  float64
 23  vote_count             45460 non-null  float64
dtypes: float64(4), object(20)
memory usage: 8.3+ MB


df_credits.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45476 entries, 0 to 45475
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   cast    45476 non-null  object
 1   crew    45476 non-null  object
 2   id      45476 non-null  int64 
dtypes: int64(1), object(2)
memory usage: 1.0+ MB


df_keywords.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 46419 entries, 0 to 46418
Data columns (total 2 columns):
 #   Column    Non-Null Count  Dtype 
---  ------    --------------  ----- 
 0   id        46419 non-null  int64 
 1   keywords  46419 non-null  object
dtypes: int64(1), object(1)
memory usage: 725.4+ KB


df_movies['id'] = pd.to_numeric(df_movies['id'], errors='coerce')


df_movies.dtypes

adult                     object
belongs_to_collection     object
budget                    object
genres                    object
homepage                  object
id                       float64
imdb_id                   object
original_language         object
original_title            object
overview                  object
popularity                object
poster_path               object
production_companies      object
production_countries      object
release_date              object
revenue                  float64
runtime                  float64
spoken_languages          object
status                    object
tagline                   object
title                     object
video                     object
vote_average             float64
vote_count               float64
dtype: object


new_df = df_movies.merge(df_credits, on='id', how='left')
df = new_df.merge(df_keywords, on='id', how='left')


df.head()


df.isnull().sum() / len(df)

adult                    0.000000
belongs_to_collection    0.901849
budget                   0.000000
genres                   0.000000
homepage                 0.828187
id                       0.000064
imdb_id                  0.000365
original_language        0.000236
original_title           0.000000
overview                 0.021337
popularity               0.000129
poster_path              0.008556
production_companies     0.000086
production_countries     0.000086
release_date             0.001887
revenue                  0.000150
runtime                  0.005811
spoken_languages         0.000150
status                   0.001909
tagline                  0.554319
title                    0.000150
video                    0.000150
vote_average             0.000150
vote_count               0.000150
cast                     0.000086
crew                     0.000086
keywords                 0.000086
dtype: float64


df.duplicated().sum()

1172


df[df.duplicated()]


df = df.drop_duplicates(keep='first')


df.duplicated().sum()

0


df.shape

(45460, 27)


df.index

Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,
                9,
            ...
            46622, 46623, 46624, 46625, 46626, 46627, 46628, 46629, 46630,
            46631],
           dtype='int64', length=45460)


df.reset_index(drop=True, inplace=True)


df.index.values

array([    0,     1,     2, ..., 45457, 45458, 45459])


df.columns

Index(['adult', 'belongs_to_collection', 'budget', 'genres', 'homepage', 'id',
       'imdb_id', 'original_language', 'original_title', 'overview',
       'popularity', 'poster_path', 'production_companies',
       'production_countries', 'release_date', 'revenue', 'runtime',
       'spoken_languages', 'status', 'tagline', 'title', 'video',
       'vote_average', 'vote_count', 'cast', 'crew', 'keywords'],
      dtype='object')


df = df.drop(columns=['homepage', 'imdb_id', 'original_title', 'poster_path', 'video', 'status'])


df['adult'].unique()

array(['False', 'True', ' - Written by Ørnås',
       ' Rune Balot goes to a casino connected to the October corporation to try to wrap up her case once and for all.',
       ' Avalanche Sharks tells the story of a bikini contest that turns into a horrifying affair when it is hit by a shark avalanche.'],
      dtype=object)


df.loc[(df['adult'] != 'True') & (df['adult'] != 'False'), 'adult'] = False
df['adult'] = df['adult'].map({'True': True, 'False': False})


df['belongs_to_collection']

0        {'id': 10194, 'name': 'Toy Story Collection', ...
1                                                      NaN
2        {'id': 119050, 'name': 'Grumpy Old Men Collect...
3                                                      NaN
4        {'id': 96871, 'name': 'Father of the Bride Col...
                               ...                        
45455                                                  NaN
45456                                                  NaN
45457                                                  NaN
45458                                                  NaN
45459                                                  NaN
Name: belongs_to_collection, Length: 45460, dtype: object


df = df.drop(columns=['belongs_to_collection'])


df.loc[:,['budget', 'revenue']]


print(f"There are {df[df['budget'] == '0']['budget'].count()} zeroes in the 'budget' column")
print(f"There are {df[df['revenue'] == 0]['revenue'].count()} zeroes in the 'revenue' column")

There are 36561 zeroes in the 'budget' column
There are 38045 zeroes in the 'revenue' column


# convert column to numeric type
df['budget'] = pd.to_numeric(df['budget'], errors = 'coerce')

df.loc[(df['budget'] == 0) & (df['revenue'] == 0), 'revenue'] = np.nan

df.loc[df['budget'] == 0, 'budget'] = np.nan


df.loc[:,['budget', 'revenue']]


def adjust_budget_revenue(number):
    if number < 100:
        return number * 1000000
    elif number >= 100 and number < 1000:
        return number * 1000
    else:
        return number


df[['budget', 'revenue']] = df[['budget', 'revenue']].applymap(adjust_budget_revenue)


sns.histplot(df[df['budget'].notnull()]['budget'], 
             kde=True, stat='density', kde_kws=dict(cut=5),
             alpha=.4, edgecolor=(1, 1, 1, .4))
plt.title("Movie budgets distribution")
plt.show()


from ast import literal_eval
def get_values(obj):
    if isinstance(obj, float):
        pass
    else:
        arr = []
        obj = literal_eval(obj)
        if isinstance(obj, list):
            for i in obj:
                arr.append(i['name'])
            return arr
        else:
            return None


df['genres'] = df['genres'].apply(get_values)
df['production_companies'] = df['production_companies'].apply(get_values)
df['production_countries'] = df['production_countries'].apply(get_values)
df['spoken_languages'] = df['spoken_languages'].apply(get_values)
df['keywords'] = df['keywords'].apply(get_values)


df.head()


def get_actors(obj):
    if isinstance(obj, float):
        pass
    else:
        arr = []
        counter = 0
        obj = literal_eval(obj)
        if isinstance(obj, list):
            for i in obj:
                if counter != 6:
                    arr.append(i['name'])
                    counter += 1
                else:
                    break
            return arr
        else:
            return None


df['cast'] = df['cast'].apply(get_actors)


def get_directors(obj):
    if isinstance(obj, float):
        pass
    else:
        arr = []
        obj = literal_eval(obj)
        if isinstance(obj, list):
            for i in obj:
                if i['job'] == 'Director':
                    arr.append(i['name'])
                    break
            return arr
        else:
            return None


df['crew'] = df['crew'].apply(get_directors)


df.head()


df['id']

0           862.0
1          8844.0
2         15602.0
3         31357.0
4         11862.0
           ...   
45455    439050.0
45456    111109.0
45457     67758.0
45458    227506.0
45459    461257.0
Name: id, Length: 45460, dtype: float64


df = df.drop(columns=['id'])


df['popularity']

0        21.946943
1        17.015539
2          11.7129
3         3.859495
4         8.387519
           ...    
45455     0.072051
45456     0.178241
45457     0.903007
45458     0.003503
45459     0.163015
Name: popularity, Length: 45460, dtype: object


df = df.drop(columns=['popularity'])


df['tagline']

0                                                      NaN
1                Roll the dice and unleash the excitement!
2        Still Yelling. Still Fighting. Still Ready for...
3        Friends are the people who let you be yourself...
4        Just When His World Is Back To Normal... He's ...
                               ...                        
45455           Rising and falling between a man and woman
45456                                                  NaN
45457                               A deadly game of wits.
45458                                                  NaN
45459                                                  NaN
Name: tagline, Length: 45460, dtype: object


df['release_date']

0        1995-10-30
1        1995-12-15
2        1995-12-22
3        1995-12-22
4        1995-02-10
            ...    
45455           NaN
45456    2011-11-17
45457    2003-08-01
45458    1917-10-21
45459    2017-06-09
Name: release_date, Length: 45460, dtype: object


df['release_date'] = pd.to_datetime(df['release_date'], infer_datetime_format=True, 
                                           format='%Y-%m-%d', errors='coerce')


df['runtime']

0         81.0
1        104.0
2        101.0
3        127.0
4        106.0
         ...  
45455     90.0
45456    360.0
45457     90.0
45458     87.0
45459     75.0
Name: runtime, Length: 45460, dtype: float64


df.loc[:, ['vote_average', 'vote_count']]


df.head()


df.to_csv("Data/Movies_Transformed.csv", index=False)


from ydata_profiling import ProfileReport
profile = ProfileReport(df, title="Movies Profile Report")
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]


# define a function to calculate countries
def count_values(df, column):
    value_count = {}
    for el in df[column].dropna():
        if len(el) > 0:
            for key in el:
                if key in value_count:
                    value_count[key] += 1
                else:
                    value_count[key] = 1
        else:
            pass
    return value_count


# make it a series
countries = pd.Series(count_values(df, 'production_countries'))


# Changing the map to a logarithmic scale because it is not representative enough in a regular scale.
ln_countries = pd.Series(np.log(countries.values), index = countries.index)


data = dict(type = 'choropleth',
           locations = ln_countries.index,
           locationmode = 'country names',
           colorscale = 'Blackbody',
           text = countries.values,
           z = ln_countries.values,
           colorbar = {'title': 'log of Sum of Movies'})

layout = dict(title = 'Distribution of producer countries',
             geo = dict(showframe = False,
                       projection = {'type': 'natural earth'}))

choromap = go.Figure(data = [data],layout = layout)
iplot(choromap, validate=False)


genres = pd.Series(count_values(df, 'genres'))
genres

Animation                                 1933
Comedy                                   13180
Family                                    2770
Adventure                                 3495
Fantasy                                   2312
Romance                                   6738
Drama                                    20263
Action                                    6594
Crime                                     4308
Thriller                                  7623
Horror                                    4674
History                                   1398
Science Fiction                           3046
Mystery                                   2465
War                                       1323
Foreign                                   1620
Music                                     1600
Documentary                               3932
Western                                   1042
TV Movie                                   769
Carousel Productions                         1
Vision View Entertainment                    1
Telescene Film Group Productions             1
Aniplex                                      1
GoHands                                      1
BROSTA TV                                    1
Mardock Scramble Production Committee        1
Sentai Filmworks                             1
Odyssey Media                                1
Pulser Productions                           1
Rogue State                                  1
The Cartel                                   1
dtype: int64


genres.sort_values(ascending=False).head(10).plot.bar(color=["red", "green", "brown", "pink", "blue", "purple",
                                                            "lightblue", "gold", "orange", "darkgreen"])
plt.title("The most popular genres")
plt.show()


# add new column to see only the year of the release
df['release_year'] = df['release_date'].apply(lambda x: x.year)
df


df[(df['release_year'] < 2018) & (df['release_year'] > 1950)]\
          .groupby('release_year').mean()['vote_average'].plot()
plt.title("The vote average score over time")
plt.show()


companies = pd.Series(count_values(df, 'production_companies'))
companies

Pixar Animation Studios          52
TriStar Pictures                197
Teitler Film                      2
Interscope Communications        36
Warner Bros.                   1250
                               ... 
He and She Films                  1
Neptune Salad Entertainment       1
Pirie Productions                 1
Sine Olivia                       1
Yermoliev                         1
Length: 23537, dtype: int64


companies.sort_values(ascending=False).head(10).plot.bar(color=["red", "green", "brown", "pink", "blue", "purple",
                                                            "lightblue", "gold", "orange", "darkgreen"])
plt.title("The most active production companies")
plt.show()


actors = pd.Series(count_values(df, 'cast'))
actors

Tom Hanks            61
Tim Allen            26
Don Rickles          14
Jim Varney           17
Wallace Shawn        33
                     ..
Bart Guingona         1
Erika Eleniak         1
Julie du Page         1
Damian Chapa          1
Aleksandr Chabrov     1
Length: 84459, dtype: int64


actors.sort_values(ascending=False).head(10).plot.bar(color=["red", "green", "brown", "pink", "blue", "purple",
                                                            "lightblue", "gold", "orange", "darkgreen"])
plt.title("Actors with the most number of movies they played in")
plt.show()


directors = pd.Series(count_values(df, 'crew'))
directors

John Lasseter       10
Joe Johnston        10
Howard Deutch       11
Forest Whitaker      4
Charles Shyer        7
                    ..
Ravi Udyawar         1
Shanra J. Kehl       1
Aaron Osborne        1
Hamid Nematollah     1
Daisy Asquith        1
Length: 17572, dtype: int64


directors.sort_values(ascending=False).head(10).plot.bar(color=["red", "green", "brown", "pink", "blue", "purple",
                                                            "lightblue", "gold", "orange", "darkgreen"])
plt.title("Directors with the most number of movies they made")
plt.show()


sns.jointplot(x = 'vote_average', y = 'budget', data = df)
sns.jointplot(x = 'vote_average', y = 'revenue', data = df)
plt.show()


sns.jointplot(x = 'vote_count', y = 'budget', data = df, kind='kde')
sns.jointplot(x = 'vote_count', y = 'revenue', data = df, kind='kde')
plt.show()


# function to calculate total profit
def count_profit(df, column_to_find, series, column_to_calculate):
    count = {}
    for i, el in zip(df.index, df[column_to_find]):
        if el != None:
            if len(el) > 0:
                for j in el:
                    for s in series.index:
                        if (s == j) and (s in count) \
                        and (np.isnan(df[column_to_calculate][i]) == False) and (df[column_to_calculate][i] != 0):
                            count[s] += df[column_to_calculate][i]
                        elif (s == j) and (s not in count) \
                        and (np.isnan(df[column_to_calculate][i]) == False) and (df[column_to_calculate][i] != 0):
                            count[s] = df[column_to_calculate][i]
    return count


# let's add a column 'profit' to our dataframe (revenue - budget)
df['profit'] = df['revenue'] - df['budget']
df['profit'].head()

0    343554033.0
1    197797249.0
2            NaN
3     65452156.0
4            NaN
Name: profit, dtype: float64


profit_count_genres = pd.Series(count_profit(df, 'genres', genres, 'profit'))
profit_count_genres.sort_values(ascending=False).head(10).plot.bar(color=["red", "green", "brown", "pink", "blue", "purple",
                                                            "lightblue", "gold", "orange", "darkgreen"])
plt.title("The most profitable genres of movies")
plt.show()


profit_companies = pd.Series(count_profit(df, 'production_companies', companies, 'profit'))
profit_companies.sort_values(ascending=False).head(10).plot.bar(color=["red", "green", "brown", "pink", "blue", "purple",
                                                            "lightblue", "gold", "orange", "darkgreen"])
plt.title("The most profitable companies")
plt.show()


(profit_count_genres / genres).sort_values(ascending=False).head(10).plot.bar(color=["red", "green", "brown", "pink", "blue", "purple",
                                                            "lightblue", "gold", "orange", "darkgreen"])
plt.title("The most profitable genres of movies in relative terms")
plt.show()


companies_with_over_100_movies = companies[companies>100]
profit_companies_over_100_movies = profit_companies[profit_companies.index.isin(companies_with_over_100_movies.index)]


(profit_companies_over_100_movies  / companies_with_over_100_movies).sort_values(ascending=False)\
                            .head(10).plot.bar(color=["red", "green", "brown", "pink", "blue", "purple",
                                                            "lightblue", "gold", "orange", "darkgreen"])
plt.title("The most profitable companies in terms of average profit per movie")
plt.show()


df['release_year_binned'] = pd.cut(df['release_year'], bins=np.linspace(1950, 2018, num=5))


sns.countplot(x = 'release_year_binned',
              data = df,
              palette = "Greens_d")
plt.title("Number of movies produced each 18 years")
plt.show()


# Calculate C - mean vote across the whole report
C = df['vote_average'].mean()

# Calculate m - minimum votes required to be listed in the Top 250. In our case to be included in the rankings, 
# a film must exceed the 90th percentile cutoff mark, denoted as "m." This means that it must receive more votes
# than at least 90% of the other movies on the list, placing it in the top 10%.
m = df['vote_count'].quantile(0.9)


# Now let's filter the movies that satisfy our 'm' parameter

# To ensure that the newly generated filtered_df DataFrame is not dependent on the original DataFrame, 
# apply the .copy() method. This way, any alterations made to the filtered_df DataFrame will not impact 
# the initial DataFrame.

filtered_df = df.copy().loc[df['vote_count'] >= m]
filtered_df.shape

(4554, 21)


# define the function to compute IMDB rating
def calculate_imdb(df, m=m, C=C):
    R = df['vote_average']
    v = df['vote_count']
    return round((v/(v+m)) * R + (m/(v+m)) * C, 2)


filtered_df['IMDB_Score'] = filtered_df.apply(calculate_imdb, axis=1)
filtered_df.sort_values(by='IMDB_Score', ascending=False, inplace=True)


filtered_df[['title', 'vote_average', 'vote_count', 'cast', 'crew', 'release_year','IMDB_Score']].head(15)


df['overview'].head()

0    Led by Woody, Andy's toys live happily in his ...
1    When siblings Judy and Peter discover an encha...
2    A family wedding reignites the ancient feud be...
3    Cheated on, mistreated and stepped on, the wom...
4    Just when George Banks has recovered from his ...
Name: overview, dtype: object


df['overview'].isnull().sum()

954


df['overview'] = df['overview'].fillna('')
df['overview'].isnull().sum()

0


from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english')
tfidf_matrix = vectorizer.fit_transform(df['overview'])
tfidf_matrix.shape

(45460, 75827)


vectorizer.get_feature_names_out()[5000:5010]

array(['avails', 'avaks', 'avalanche', 'avalanches', 'avallone', 'avalon',
       'avant', 'avanthika', 'avanti', 'avaracious'], dtype=object)


from sklearn.metrics.pairwise import linear_kernel

# Compute the cosine similarity matrix
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)


cosine_sim.shape

(45460, 45460)


cosine_sim[1:3]

array([[0.01503934, 1.        , 0.04680902, ..., 0.        , 0.0219836 ,
        0.00928956],
       [0.        , 0.04680902, 1.        , ..., 0.        , 0.01402398,
        0.        ]])


df['title'].duplicated().sum()

3182


indices = pd.Series(index = df['title'], data = df.index)
indices

title
Toy Story                          0
Jumanji                            1
Grumpier Old Men                   2
Waiting to Exhale                  3
Father of the Bride Part II        4
                               ...  
Subdue                         45455
Century of Birthing            45456
Betrayal                       45457
Satan Triumphant               45458
Queerama                       45459
Length: 45460, dtype: int64


# Function that takes in movie title as input and outputs most similar movies
def get_recommendations(title, cosine_sim=cosine_sim):
    # Get the index of the movie that matches the title
    idx = indices[title]
    
    # Get the pairwise similarity scores of all movies with that movie
    sim_scores = list(enumerate(cosine_sim[idx]))
    
    # Sort the movies based on the similarity scores
    sim_scores = sorted(sim_scores, key=lambda x : x[1], reverse=True)

    # Get the scores of the 10 most similar movies
    # Starting with 1 because index 0 will give the same name of the input title 
    sim_scores = sim_scores[1:11]
    
    # Get the movie indices
    movie_indices = [i[0] for i in sim_scores]
    
    # Return the top 10 most similar movies
    return df['title'][movie_indices]


get_recommendations("Fight Club")

15462    9/11: The Twin Towers
31845           Unknown Caller
5204                 Delirious
23954           Make Your Move
22905                  In Fear
28431                    Julia
30239               Freerunner
5338                   Perfect
36048             Frankenstein
12497               Dead Awake
Name: title, dtype: object


# define a function to remove spaces and to lowercase
def clean_data(obj):
    if isinstance(obj, list):
        return [i.replace(" ", "").lower() for i in obj]
    elif isinstance(obj, str):
        return obj.replace(" ", "").lower()
    else:
        return ""


# Now let's apply this function to our columns
cols = ['genres', 'cast', 'crew', 'keywords']
for col in cols:
    df[col] = df[col].apply(clean_data)
df[cols].head()


df['overview'] = df['overview'].apply(lambda x : x.split())
# fill na values with empty string
df['tagline'] = df['tagline'].fillna("")
df['tagline'] = df['tagline'].apply(lambda x : x.split())


df.head()


cols = ['overview', 'tagline']
for col in cols:
    df[col] = df[col].apply(clean_data)
df[cols].head()


def create_tags(x):
    return ' '.join(x['overview']) + ' ' + ' '.join(x['keywords']) + ' ' + ' '.join(x['cast']) + ' '\
           + ' '.join(x['crew']) + ' ' + ' '.join(x['genres']) + ' ' + ' '.join(x['tagline'])


df['tags'] = df.apply(create_tags, axis=1)


df['tags'].head()

0    led by woody, andy's toys live happily in his ...
1    when siblings judy and peter discover an encha...
2    a family wedding reignites the ancient feud be...
3    cheated on, mistreated and stepped on, the wom...
4    just when george banks has recovered from his ...
Name: tags, dtype: object


# create new table with just title and tags columns
new_df = df[['title', 'tags']]
new_df.head()


from sklearn.feature_extraction.text import CountVectorizer

count_vectorizer = CountVectorizer(stop_words='english')
count_matrix = count_vectorizer.fit_transform(new_df['tags'])
count_matrix.shape

(45460, 187640)


from sklearn.metrics.pairwise import cosine_similarity
cosine_sim_new = cosine_similarity(count_matrix, count_matrix)
cosine_sim_new.shape

(45460, 45460)


indices_new = pd.Series(index=new_df['title'], data = new_df.index)
indices_new.head()

title
Toy Story                      0
Jumanji                        1
Grumpier Old Men               2
Waiting to Exhale              3
Father of the Bride Part II    4
dtype: int64


def get_improved_recommendations(title, cosine_sim=cosine_sim_new):
    idx = indices_new[title]
    
    sim_scores = list(enumerate(cosine_sim[idx]))
    
    sim_scores = sorted(sim_scores, key=lambda x : x[1], reverse=True)

    sim_scores = sim_scores[1:11]
    
    movie_indices = [i[0] for i in sim_scores]
    
    return new_df['title'][movie_indices]


get_improved_recommendations('Interstellar')

44071                      Space Station 3D
8644     Journey to the Far Side of the Sun
44214                      Manhunt in Space
3704                          Space Cowboys
36052                      The War in Space
23035          Space Pirate Captain Harlock
4727                       Destiny in Space
36456                             Project-M
1512                                Contact
10675              Voices of a Distant Star
Name: title, dtype: object


print(f"Initial Movie Recommender:\n{get_recommendations('Fight Club')}")
print("--------------------------------")
print(f"Improved Movie Recommender:\n{get_improved_recommendations('Fight Club')}")

Initial Movie Recommender:
15462    9/11: The Twin Towers
31845           Unknown Caller
5204                 Delirious
23954           Make Your Move
22905                  In Fear
28431                    Julia
30239               Freerunner
5338                   Perfect
36048             Frankenstein
12497               Dead Awake
Name: title, dtype: object
--------------------------------
Improved Movie Recommender:
32258                             Joni
12491                  Never Back Down
38867           Emperor Tomato Ketchup
10905       The Brotherhood of Justice
36662    Egomania: Island Without Hope
38580                  East Side Sushi
13601              The Small Back Room
36              Across the Sea of Time
21613            This Is Martin Bonner
28669                      The Cobbler
Name: title, dtype: object


print(f'Initial Movie Recommender:\n{get_recommendations("Star Wars")}')
print("--------------------------------")
print(f'Improved Movie Recommender:\n{get_improved_recommendations("Star Wars")}')

Initial Movie Recommender:
1155                               The Empire Strikes Back
30431                        The Star Wars Holiday Special
26552                         Star Wars: The Force Awakens
1168                                    Return of the Jedi
34152           Samson and the Seven Miracles of the World
1268                                          Mad Dog Time
5188                                   The Triumph of Love
37832                                         Call to Arms
25101    1½ Knights - In Search of the Ravishing Prince...
24386                                      Sleeping Beauty
Name: title, dtype: object
--------------------------------
Improved Movie Recommender:
30431                   The Star Wars Holiday Special
1155                          The Empire Strikes Back
26552                    Star Wars: The Force Awakens
1168                               Return of the Jedi
22937                              Threads of Destiny
10070    Star Wars: Episode III - Revenge of the Sith
8852                                  The Ice Pirates
2514        Star Wars: Episode I - The Phantom Menace
32607                                  Humanity's End
41484                    Rogue One: A Star Wars Story
Name: title, dtype: object

	cast	crew	id
0	[{'cast_id': 14, 'character': 'Woody (voice)',...	[{'credit_id': '52fe4284c3a36847f8024f49', 'de...	862
1	[{'cast_id': 1, 'character': 'Alan Parrish', '...	[{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...	8844
2	[{'cast_id': 2, 'character': 'Max Goldman', 'c...	[{'credit_id': '52fe466a9251416c75077a89', 'de...	15602
3	[{'cast_id': 1, 'character': "Savannah 'Vannah...	[{'credit_id': '52fe44779251416c91011acb', 'de...	31357
4	[{'cast_id': 1, 'character': 'George Banks', '...	[{'credit_id': '52fe44959251416c75039ed7', 'de...	11862

	adult	belongs_to_collection	budget	genres	homepage	id	imdb_id	original_language	original_title	overview	...	spoken_languages	status	tagline	title	video	vote_average	vote_count	cast	crew	keywords
677	False	NaN	0	[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...	NaN	105045.0	tt0111613	de	Das Versprechen	East-Berlin, 1961, shortly after the erection ...	...	[{'iso_639_1': 'de', 'name': 'Deutsch'}]	Released	A love, a hope, a wall.	The Promise	False	5.0	1.0	[{'cast_id': 5, 'character': 'Sophie II', 'cre...	[{'credit_id': '52fe4a44c3a36847f81c463f', 'de...	[{'id': 7059, 'name': 'anti-communism'}, {'id'...
678	False	NaN	0	[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...	NaN	105045.0	tt0111613	de	Das Versprechen	East-Berlin, 1961, shortly after the erection ...	...	[{'iso_639_1': 'de', 'name': 'Deutsch'}]	Released	A love, a hope, a wall.	The Promise	False	5.0	1.0	[{'cast_id': 5, 'character': 'Sophie II', 'cre...	[{'credit_id': '52fe4a44c3a36847f81c463f', 'de...	[{'id': 7059, 'name': 'anti-communism'}, {'id'...
679	False	NaN	0	[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...	NaN	105045.0	tt0111613	de	Das Versprechen	East-Berlin, 1961, shortly after the erection ...	...	[{'iso_639_1': 'de', 'name': 'Deutsch'}]	Released	A love, a hope, a wall.	The Promise	False	5.0	1.0	[{'cast_id': 5, 'character': 'Sophie II', 'cre...	[{'credit_id': '52fe4a44c3a36847f81c463f', 'de...	[{'id': 7059, 'name': 'anti-communism'}, {'id'...
842	False	NaN	0	[{'id': 18, 'name': 'Drama'}]	NaN	132641.0	tt0046468	ja	Tsuma	Ten years into a marriage, the wife is disappo...	...	[{'iso_639_1': 'ja', 'name': '日本語'}]	Released	NaN	Wife	False	0.0	0.0	[{'cast_id': 4, 'character': 'Mihoko Nakagawa'...	[{'credit_id': '52fe4b9ac3a368484e190d25', 'de...	[{'id': 818, 'name': 'based on novel'}, {'id':...
844	False	NaN	0	[{'id': 18, 'name': 'Drama'}]	NaN	132641.0	tt0046468	ja	Tsuma	Ten years into a marriage, the wife is disappo...	...	[{'iso_639_1': 'ja', 'name': '日本語'}]	Released	NaN	Wife	False	0.0	0.0	[{'cast_id': 4, 'character': 'Mihoko Nakagawa'...	[{'credit_id': '56365ed9925141285701b06e', 'de...	[{'id': 818, 'name': 'based on novel'}, {'id':...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
45989	False	{'id': 34055, 'name': 'Pokémon Collection', 'p...	0	[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...	http://www.pokemon.com/us/movies/movie-pokemon...	12600.0	tt0287635	ja	劇場版ポケットモンスターセレビィ時を越えた遭遇（であい）	All your favorite Pokémon characters are back,...	...	[{'iso_639_1': 'ja', 'name': '日本語'}]	Released	NaN	Pokémon 4Ever: Celebi - Voice of the Forest	False	5.7	82.0	[{'cast_id': 1, 'character': 'Ash Ketchum (voi...	[{'credit_id': '52fe45049251416c75048e35', 'de...	[{'id': 9663, 'name': 'sequel'}, {'id': 11451,...
46428	False	NaN	0	[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...	NaN	265189.0	tt2121382	sv	Turist	While holidaying in the French Alps, a Swedish...	...	[{'iso_639_1': 'fr', 'name': 'Français'}, {'is...	Released	NaN	Force Majeure	False	6.8	255.0	[{'cast_id': 2, 'character': 'Ebba', 'credit_i...	[{'credit_id': '534fd1a80e0a267eb6000e32', 'de...	[{'id': 293, 'name': 'female nudity'}, {'id': ...
46429	False	NaN	0	[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...	NaN	265189.0	tt2121382	sv	Turist	While holidaying in the French Alps, a Swedish...	...	[{'iso_639_1': 'fr', 'name': 'Français'}, {'is...	Released	NaN	Force Majeure	False	6.8	255.0	[{'cast_id': 2, 'character': 'Ebba', 'credit_i...	[{'credit_id': '534fd1a80e0a267eb6000e32', 'de...	[{'id': 293, 'name': 'female nudity'}, {'id': ...
46430	False	NaN	0	[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...	NaN	265189.0	tt2121382	sv	Turist	While holidaying in the French Alps, a Swedish...	...	[{'iso_639_1': 'fr', 'name': 'Français'}, {'is...	Released	NaN	Force Majeure	False	6.8	255.0	[{'cast_id': 2, 'character': 'Ebba', 'credit_i...	[{'credit_id': '534fd1a80e0a267eb6000e32', 'de...	[{'id': 293, 'name': 'female nudity'}, {'id': ...
46431	False	NaN	0	[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...	NaN	265189.0	tt2121382	sv	Turist	While holidaying in the French Alps, a Swedish...	...	[{'iso_639_1': 'fr', 'name': 'Français'}, {'is...	Released	NaN	Force Majeure	False	6.8	255.0	[{'cast_id': 2, 'character': 'Ebba', 'credit_i...	[{'credit_id': '534fd1a80e0a267eb6000e32', 'de...	[{'id': 293, 'name': 'female nudity'}, {'id': ...

	title	vote_average	vote_count	cast	crew	release_year	IMDB_Score
314	The Shawshank Redemption	8.5	8358.0	[Tim Robbins, Morgan Freeman, Bob Gunton, Clan...	[Frank Darabont]	1994.0	8.45
834	The Godfather	8.5	6024.0	[Marlon Brando, Al Pacino, James Caan, Richard...	[Francis Ford Coppola]	1972.0	8.43
10310	Dilwale Dulhania Le Jayenge	9.1	661.0	[Shah Rukh Khan, Kajol, Amrish Puri, Anupam Kh...	[Aditya Chopra]	1995.0	8.42
12482	The Dark Knight	8.3	12269.0	[Christian Bale, Michael Caine, Heath Ledger, ...	[Christopher Nolan]	2008.0	8.27
2843	Fight Club	8.3	9678.0	[Edward Norton, Brad Pitt, Meat Loaf, Jared Le...	[David Fincher]	1999.0	8.26
292	Pulp Fiction	8.3	8670.0	[John Travolta, Samuel L. Jackson, Uma Thurman...	[Quentin Tarantino]	1994.0	8.25
23671	Whiplash	8.3	4376.0	[Miles Teller, J.K. Simmons, Melissa Benoist, ...	[Damien Chazelle]	2014.0	8.21
522	Schindler's List	8.3	4436.0	[Liam Neeson, Ben Kingsley, Ralph Fiennes, Car...	[Steven Spielberg]	1993.0	8.21
5482	Spirited Away	8.3	3968.0	[Rumi Hiiragi, Miyu Irino, Mari Natsuki, Bunta...	[Hayao Miyazaki]	2001.0	8.20
2211	Life Is Beautiful	8.3	3643.0	[Nicoletta Braschi, Roberto Benigni, Giorgio C...	[Roberto Benigni]	1997.0	8.19
1179	The Godfather: Part II	8.3	3418.0	[Al Pacino, Robert Duvall, Diane Keaton, Rober...	[Francis Ford Coppola]	1974.0	8.18
1153	One Flew Over the Cuckoo's Nest	8.3	3001.0	[Jack Nicholson, Louise Fletcher, Danny DeVito...	[Miloš Forman]	1975.0	8.16
351	Forrest Gump	8.2	8147.0	[Tom Hanks, Robin Wright, Gary Sinise, Mykelti...	[Robert Zemeckis]	1994.0	8.15
18466	The Intouchables	8.2	5410.0	[François Cluzet, Omar Sy, Audrey Fleurot, Ann...	[Eric Toledano]	2011.0	8.13
1177	Psycho	8.3	2405.0	[Anthony Perkins, Vera Miles, John Gavin, Jane...	[Alfred Hitchcock]	1960.0	8.13

	genres	cast	crew	keywords
0	[animation, comedy, family]	[tomhanks, timallen, donrickles, jimvarney, wa...	[johnlasseter]	[jealousy, toy, boy, friendship, friends, riva...
1	[adventure, fantasy, family]	[robinwilliams, jonathanhyde, kirstendunst, br...	[joejohnston]	[boardgame, disappearance, basedonchildren'sbo...
2	[romance, comedy]	[waltermatthau, jacklemmon, ann-margret, sophi...	[howarddeutch]	[fishing, bestfriend, duringcreditsstinger, ol...
3	[comedy, drama, romance]	[whitneyhouston, angelabassett, lorettadevine,...	[forestwhitaker]	[basedonnovel, interracialrelationship, single...
4	[comedy]	[stevemartin, dianekeaton, martinshort, kimber...	[charlesshyer]	[baby, midlifecrisis, confidence, aging, daugh...

	adult	budget	genres	original_language	overview	production_companies	production_countries	release_date	revenue	runtime	...	tagline	title	vote_average	vote_count	cast	crew	keywords	release_year	profit	release_year_binned
0	False	30000000.0	[animation, comedy, family]	en	[Led, by, Woody,, Andy's, toys, live, happily,...	[Pixar Animation Studios]	[United States of America]	1995-10-30	373554033.0	81.0	...	[]	Toy Story	7.7	5415.0	[tomhanks, timallen, donrickles, jimvarney, wa...	[johnlasseter]	[jealousy, toy, boy, friendship, friends, riva...	1995.0	343554033.0	(1984.0, 2001.0]
1	False	65000000.0	[adventure, fantasy, family]	en	[When, siblings, Judy, and, Peter, discover, a...	[TriStar Pictures, Teitler Film, Interscope Co...	[United States of America]	1995-12-15	262797249.0	104.0	...	[Roll, the, dice, and, unleash, the, excitement!]	Jumanji	6.9	2413.0	[robinwilliams, jonathanhyde, kirstendunst, br...	[joejohnston]	[boardgame, disappearance, basedonchildren'sbo...	1995.0	197797249.0	(1984.0, 2001.0]
2	False	NaN	[romance, comedy]	en	[A, family, wedding, reignites, the, ancient, ...	[Warner Bros., Lancaster Gate]	[United States of America]	1995-12-22	NaN	101.0	...	[Still, Yelling., Still, Fighting., Still, Rea...	Grumpier Old Men	6.5	92.0	[waltermatthau, jacklemmon, ann-margret, sophi...	[howarddeutch]	[fishing, bestfriend, duringcreditsstinger, ol...	1995.0	NaN	(1984.0, 2001.0]
3	False	16000000.0	[comedy, drama, romance]	en	[Cheated, on,, mistreated, and, stepped, on,, ...	[Twentieth Century Fox Film Corporation]	[United States of America]	1995-12-22	81452156.0	127.0	...	[Friends, are, the, people, who, let, you, be,...	Waiting to Exhale	6.1	34.0	[whitneyhouston, angelabassett, lorettadevine,...	[forestwhitaker]	[basedonnovel, interracialrelationship, single...	1995.0	65452156.0	(1984.0, 2001.0]
4	False	NaN	[comedy]	en	[Just, when, George, Banks, has, recovered, fr...	[Sandollar Productions, Touchstone Pictures]	[United States of America]	1995-02-10	76578911.0	106.0	...	[Just, When, His, World, Is, Back, To, Normal....	Father of the Bride Part II	5.7	173.0	[stevemartin, dianekeaton, martinshort, kimber...	[charlesshyer]	[baby, midlifecrisis, confidence, aging, daugh...	1995.0	NaN	(1984.0, 2001.0]

Movie Recommendation System¶

Introduction¶

Importing libraries¶

Reading the data and getting familiar with it¶

Column analysis¶

adult¶

belongs_to_collection¶

budget & revenue¶

genres, production_companies, production_countries, and spoken_languages¶

id¶

popularity¶

tagline¶

release_date¶

runtime¶

vote_average and vote_count¶

Exploratory Data Analysis¶

Distribution of producer countries¶

The most popular genres¶

The vote average over the years¶

The most active production companies¶

The most active actors¶

The most active directors¶

Relationships among `budget`, `revenue`, `vote_average`, and `vote_count` variables¶

The most profitable movies¶

Most profitable genres and companies in terms of average profit generated per movie¶

Number of movies produced in year¶

Simple Movie Recommender¶

How is the movie weighted rating calculated according to IMDB¶

Content Based Recommendation System¶

Conclusion¶

	adult	belongs_to_collection	budget	genres	homepage	id	imdb_id	original_language	original_title	overview	...	release_date	revenue	runtime	spoken_languages	status	tagline	title	video	vote_average	vote_count
0	False	{'id': 10194, 'name': 'Toy Story Collection', ...	30000000	[{'id': 16, 'name': 'Animation'}, {'id': 35, '...	http://toystory.disney.com/toy-story	862	tt0114709	en	Toy Story	Led by Woody, Andy's toys live happily in his ...	...	1995-10-30	373554033.0	81.0	[{'iso_639_1': 'en', 'name': 'English'}]	Released	NaN	Toy Story	False	7.7	5415.0
1	False	NaN	65000000	[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...	NaN	8844	tt0113497	en	Jumanji	When siblings Judy and Peter discover an encha...	...	1995-12-15	262797249.0	104.0	[{'iso_639_1': 'en', 'name': 'English'}, {'iso...	Released	Roll the dice and unleash the excitement!	Jumanji	False	6.9	2413.0
2	False	{'id': 119050, 'name': 'Grumpy Old Men Collect...	0	[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...	NaN	15602	tt0113228	en	Grumpier Old Men	A family wedding reignites the ancient feud be...	...	1995-12-22	0.0	101.0	[{'iso_639_1': 'en', 'name': 'English'}]	Released	Still Yelling. Still Fighting. Still Ready for...	Grumpier Old Men	False	6.5	92.0
3	False	NaN	16000000	[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...	NaN	31357	tt0114885	en	Waiting to Exhale	Cheated on, mistreated and stepped on, the wom...	...	1995-12-22	81452156.0	127.0	[{'iso_639_1': 'en', 'name': 'English'}]	Released	Friends are the people who let you be yourself...	Waiting to Exhale	False	6.1	34.0
4	False	{'id': 96871, 'name': 'Father of the Bride Col...	0	[{'id': 35, 'name': 'Comedy'}]	NaN	11862	tt0113041	en	Father of the Bride Part II	Just when George Banks has recovered from his ...	...	1995-02-10	76578911.0	106.0	[{'iso_639_1': 'en', 'name': 'English'}]	Released	Just When His World Is Back To Normal... He's ...	Father of the Bride Part II	False	5.7	173.0

	id	keywords
0	862	[{'id': 931, 'name': 'jealousy'}, {'id': 4290,...
1	8844	[{'id': 10090, 'name': 'board game'}, {'id': 1...
2	15602	[{'id': 1495, 'name': 'fishing'}, {'id': 12392...
3	31357	[{'id': 818, 'name': 'based on novel'}, {'id':...
4	11862	[{'id': 1009, 'name': 'baby'}, {'id': 1599, 'n...

Movie Recommendation System¶

Introduction¶

Importing libraries¶

Reading the data and getting familiar with it¶

Column analysis¶

adult¶

belongs_to_collection¶

budget & revenue¶

genres, production_companies, production_countries, and spoken_languages¶

id¶

popularity¶

tagline¶

release_date¶

runtime¶

vote_average and vote_count¶

Exploratory Data Analysis¶

Distribution of producer countries¶

The most popular genres¶

The vote average over the years¶

The most active production companies¶

The most active actors¶

The most active directors¶

Relationships among budget, revenue, vote_average, and vote_count variables¶

The most profitable movies¶

Most profitable genres and companies in terms of average profit generated per movie¶

Number of movies produced in year¶

Simple Movie Recommender¶

How is the movie weighted rating calculated according to IMDB¶

Content Based Recommendation System¶

Conclusion¶

Relationships among `budget`, `revenue`, `vote_average`, and `vote_count` variables¶